# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# ==============================================================================
# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
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#     http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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"""Functions to override model parameters from command-line flags."""

from absl import logging
from hyperparameters import params_dict

ESSENTIAL_FLAGS = ['tpu', 'data_dir', 'model_dir']


def override_params_from_input_flags(params, input_flags):
    """Update params dictionary with input flags.

    Args:
      params: ParamsDict object containing dictionary of model parameters.
      input_flags: All the flags with non-null value of overridden model
      parameters.

    Returns:
      ParamsDict object containing dictionary of model parameters.
    """
    if not isinstance(params, params_dict.ParamsDict):
        raise ValueError(
            'The base parameter set must be a ParamsDict, was: {}'.format(
                type(params)))

    essential_flag_dict = {}
    for key in ESSENTIAL_FLAGS:
        flag_value = input_flags.get_flag_value(key, None)

        if flag_value is None:
            logging.warning('Flag %s is None.', key)
        else:
            essential_flag_dict[key] = flag_value

    params_dict.override_params_dict(params,
                                     essential_flag_dict,
                                     is_strict=False)

    normal_flag_dict = get_dictionary_from_flags(params.as_dict(), input_flags)

    params_dict.override_params_dict(params,
                                     normal_flag_dict,
                                     is_strict=False)

    return params


def get_dictionary_from_flags(params, input_flags):
    """Generate dictionary from non-null flags.

    Args:
      params: Python dictionary of model parameters.
      input_flags: All the flags with non-null value of overridden model
      parameters.

    Returns:
      Python dict of overriding model parameters.
    """
    if not isinstance(params, dict):
        raise ValueError('The base parameter set must be a dict. '
                         'Was: {}'.format(type(params)))
    flag_dict = {}
    for k, v in params.items():
        if isinstance(v, dict):
            d = get_dictionary_from_flags(v, input_flags)
            flag_dict[k] = d
        else:
            try:
                flag_value = input_flags.get_flag_value(k, None)
                if flag_value is not None:
                    flag_dict[k] = flag_value
            except AttributeError:
                flag_dict[k] = v

    return flag_dict
